code: def tokenize_sentence(self, tokens, add_service_tokens=True):
      result = []
      is_word_start = []
      for token in tokens:
          pieces = self.tokenizer.tokenize(token)
          result.extend(pieces)
          starts = [False] * len(pieces)
          starts[0] = True
          is_word_start.extend(starts)
  
      if add_service_tokens:
          if len(result) > self.max_length - 2:
              result = result[: self.max_length - 2]
              is_word_start = is_word_start[: self.max_length - 2]
  
          result = ["[CLS]"] + result + ["[SEP]"]
          is_word_start = [False] + is_word_start + [False]
      else:
          if len(result) > self.max_length:
              result = result[: self.max_length]
              is_word_start = is_word_start[: self.max_length]
  
      return (result, is_word_start)
  
imports: import shutil
  import numpy
  import plyvel
  import os.path
  import sys
  from bert.modeling import *
  from bert.tokenization import *
  import json
  import os.path
  import numpy
label_desc: Use image node_id 2 for calling the BertEmbeddingsResolver obj's underlying member method code with example usage: obj.tokenize_sentence(tokens, add_service_tokens) and returns: result,  is_word_start